[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$f2KYRMKtgGIpDl3Trc0ucw1jBGQsqzY--rbEAMqZ-L6c":3},{"article":4,"iocs":49,"watch_terms":54,"solutions":55,"lesson":114},{"id":5,"title":6,"slug":7,"summary":8,"ai_summary":9,"brief":10,"url":11,"image_url":12,"published_at":13,"ingested_at":14,"relevance_score":15,"entities":16,"category_id":26,"category":27,"article_tags":31},"6b04431e-0e4f-4154-854b-a97515fee1ff","The next election influence campaign will target your AI chatbot","the-next-election-influence-campaign-will-target-your-ai-chatbot-eda445","Someone is seeding the internet with hyperlocal content about political races. Researchers say it’s designed to train AI chatbots to favor candidates across a broad range of issues. The post The next election influence campaign will target your AI chatbot appeared first on CyberScoop.","A new influence campaign is using networks of hyperlocal websites to train AI chatbots to favor specific political candidates. These sites, designed for AI crawlers rather than human readers, push biased content on issues like housing and taxes. Researchers believe this tactic aims to subtly shape AI-generated responses to user queries about local races.","Influence campaign seeds internet with hyperlocal content to train AI chatbots favoring specific candidates.","https:\u002F\u002Fcyberscoop.com\u002Fai-chatbots-election-influence-hyperlocal-networks\u002F","https:\u002F\u002Fcyberscoop.com\u002Fwp-content\u002Fuploads\u002Fsites\u002F3\u002F2026\u002F10\u002FGettyImages-2263341708.jpg","2026-10-15T19:29:58+00:00","2026-10-15T20:00:24.532073+00:00",8,[17,20,23],{"name":18,"type":19},"VisionHeight","vendor",{"name":21,"type":22},"Perplexity","product",{"name":24,"type":25},"AI chatbot","technology","839da5c1-3c34-47e2-9499-f7201640e3ac",{"id":26,"icon":28,"name":29,"slug":30},null,"AI Security","ai-security",[32,37,39,44],{"category":33},{"id":34,"icon":28,"name":35,"slug":36},"6cbdd207-aaa1-4176-9534-e156b125e917","Nation-state","nation-state",{"category":38},{"id":26,"icon":28,"name":29,"slug":30},{"category":40},{"id":41,"icon":28,"name":42,"slug":43},"c5c77cdb-f7d7-4990-9436-c81dcbff1163","Policy","policy",{"category":45},{"id":46,"icon":28,"name":47,"slug":48},"e7b231c8-5f79-4465-8d38-1ef13aea5a14","Threat Intelligence","threat-intelligence",[50],{"type":51,"value":52,"context":53},"domain","tarheelstatereport.com","Example of a targeted website linked to AI model Perplexity",[18,21],{"controls":56,"vendors":113},[57,85],{"slug":48,"name":58,"problem":59,"products":60},"Threat intelligence","Attack campaigns reach your sector, suppliers or software before your team hears about them.",[61,69,77],{"vendor_slug":62,"vendor_name":63,"vendor_logo":64,"product_slug":65,"product_name":66,"url":67,"description":68},"google","Google","\u002Fvendor-logos\u002Fgoogle.png","google-threat-intelligence","Google Threat Intelligence","https:\u002F\u002Fcloud.google.com\u002Fsecurity\u002Fproducts\u002Fthreat-intelligence","Threat intelligence from Mandiant's frontline investigations, Google's threat analysts, open sources and Google's own visibility, in one service.",{"vendor_slug":70,"vendor_name":71,"vendor_logo":72,"product_slug":73,"product_name":74,"url":75,"description":76},"crowdstrike","CrowdStrike","\u002Fvendor-logos\u002Fcrowdstrike.png","falcon-adversary-intelligence","Falcon Adversary Intelligence","https:\u002F\u002Fwww.crowdstrike.com\u002Fen-us\u002Fplatform\u002Fthreat-intelligence\u002F","Threat intelligence on the adversaries CrowdStrike tracks, built into the Falcon platform to detect, disrupt and defend against them.",{"vendor_slug":78,"vendor_name":79,"vendor_logo":80,"product_slug":81,"product_name":82,"url":83,"description":84},"dataminr","Dataminr","\u002Fvendor-logos\u002Fdataminr.png","dataminr-threat-intelligence","Dataminr Threat Intelligence","https:\u002F\u002Fwww.dataminr.com\u002Fproducts\u002Fcyber-defense\u002Fthreat-intelligence\u002F","Real-time alerts on emerging cyber threats, exploited vulnerabilities and attacks relevant to your organisation, detected with AI across a large set of public data sources.",{"slug":30,"name":86,"problem":87,"products":88},"AI application security","Prompt injection, data leaks through AI tools and unapproved AI use get caught.",[89,97,105],{"vendor_slug":90,"vendor_name":91,"vendor_logo":92,"product_slug":93,"product_name":94,"url":95,"description":96},"palo-alto-networks","Palo Alto Networks","\u002Fvendor-logos\u002Fpalo-alto-networks.png","prisma-airs","Prisma AIRS","https:\u002F\u002Fwww.paloaltonetworks.com\u002Fai-security\u002Fprisma-airs","Security for AI applications, models and agents, including runtime protection against prompt injection.",{"vendor_slug":98,"vendor_name":99,"vendor_logo":100,"product_slug":101,"product_name":102,"url":103,"description":104},"cisco","Cisco","\u002Fvendor-logos\u002Fcisco.png","ai-defense","Cisco AI Defense","https:\u002F\u002Fwww.cisco.com\u002Fsite\u002Fus\u002Fen\u002Fproducts\u002Fsecurity\u002Fai-defense\u002Findex.html","Discovery of AI use, testing of models and guardrails for AI applications.",{"vendor_slug":106,"vendor_name":107,"vendor_logo":108,"product_slug":109,"product_name":110,"url":111,"description":112},"hiddenlayer","HiddenLayer","\u002Fvendor-logos\u002Fhiddenlayer.png","hiddenlayer-ai-security","HiddenLayer AI Security Platform","https:\u002F\u002Fhiddenlayer.com\u002F","Model scanning, AI red teaming and runtime protection for AI applications.",[],{"slug":115,"title":116,"body":117,"framework_refs":118,"framework_notes":128},"ai-chatbots-targeted-by-covert-political-influence-networks","AI Chatbots Targeted by Covert Political Influence Networks","Adversarial actors are creating networks of hyperlocal, AI-crawler-optimized websites designed to poison the training and retrieval data that large language models rely on, subtly biasing their outputs toward specific political candidates or positions. This represents a supply chain attack on the information ecosystem — rather than hacking a system directly, attackers corrupt the data sources AI systems trust. Because most users treat AI-generated responses as authoritative and neutral, this manipulation is especially dangerous during election cycles. The threat highlights a critical gap: organizations and platform providers lack sufficient transparency and auditing controls over what content AI systems ingest and surface.",[119,120,121,122,123,124,125,126,127],"NIST AI RMF – GOVERN 1.1 (Policies for AI risk management)","NIST AI RMF – MAP 2.3 (Scientific integrity and TEVV for AI data)","CIS Control 3 – Data Protection","CIS Control 14 – Security Awareness and Skills Training","NIST SP 800-218 – Secure Software Development Framework (Supply Chain)","EU AI Act – Article 13 (Transparency and provision of information to users)","EU AI Act – Article 9 (Risk management system for high-risk AI)","NIST CSF DE.CM-7 (Monitoring for unauthorized activity)","GDPR Article 22 – Automated individual decision-making",{}]